Fluidity

About Fluidity

After the collapse of the 20th-century systematic mode of social organization, how can we move from our internet-enabled atomized mode, toward a fluid mode? We take problems of meaning-making, typically considered spiritual, and turn them into practical problems, which are more tractable. "Meaningness" begins with this episode: https://fluidity.libsyn.com/an-appetizer-purpose "Meaningness And Time" begins with this episode: https://fluidity.libsyn.com/meaningness-and-time-how-meaning-fell-apart 'In The Cells Of The Eggplant" begins with this episode: https://fluidity.libsyn.com/intro-to-metarationality You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold This is a nonfiction audiobook narrated by Matt Arnold with the permission of the author, David Chapman. Full text at: https://meaningness.com Please email me at fluidity@hey.com.

Publisher

Matt Arnold

Category

science · society & culture

Language

en

Explicit

No

First episode

17 Jan 2021

Latest episode

19 Jan 2025

Latest episodes

154 episodes in the feed.

1. 

   19 Jan 2025

   A Better Future, Without Backprop

   This concludes "Gradient Dissent", the companion document to "Better Without AI". Thank you so much for listening! You can support the podcast and get episodes a week early, by supporting the Patreon: https://www.patreon.com/m/fluidityaudiobooks If you like the show, consider buying me a coffee: https://www.buymeacoffee.com/mattarnold Original music by Kevin MacLeod. This podcast is under a Creative Commons Attribution Non-Commercial International 4.0 License.

2. 

   12 Jan 2025

   Better Text Generation With Science And Engineering

   David Chapman explores how advancements in retrieval-based language models could revolutionize text generation by offering a more reliable, efficient, and cost-effective alternative to current methods.

3. 

   5 Jan 2025

   Classifying Images: Massive Parallelism And Surface Features

   Analysis of image classifiers demonstrates that it is possible to understand backprop networks at the task-relevant run-time algorithmic level. In these systems, at least, networks gain their power from deploying massive parallelism to check for the presence of a vast number of simple, shallow patterns. https://betterwithout.ai/images-surface-features This episode has a lot of links: David Chapman's earliest public mention, in February 2016, of image classifiers probably using color and texture in ways that "cheat": twitter.com/Meaningness/status/698688687341572096 Jordana Cepelewicz’s “Where we see shapes, AI sees textures,” Quanta Magazine, July 1, 2019: https://www.quantamagazine.org/where-we-see-shapes-ai-sees-textures-20190701/ “Suddenly, a leopard print sofa appears”, May 2015: https://web.archive.org/web/20150622084852/http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html “Understanding How Image Quality Affects Deep Neural Networks” April 2016: https://arxiv.org/abs/1604.04004 Goodfellow et al., “Explaining and Harnessing Adversarial Examples,” December 2014: https://arxiv.org/abs/1412.6572 “Universal adversarial perturbations,” October 2016: https://arxiv.org/pdf/1610.08401v1.pdf “Exploring the Landscape of Spatial Robustness,” December 2017: https://arxiv.org/abs/1712.02779 “Overinterpretation reveals image classification model pathologies,” NeurIPS 2021: https://proceedings.neurips.cc/paper/2021/file/8217bb4e7fa0541e0f5e04fea764ab91-Paper.pdf “Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet,” ICLR 2019: https://openreview.net/forum?id=SkfMWhAqYQ Baker et al.’s “Deep convolutional networks do not classify based on global object shape,” PLOS Computational Biology, 2018: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006613 François Chollet's Twitter threads about AI producing images of horses with extra legs: twitter.com/fchollet/status/1573836241875120128 and twitter.com/fchollet/status/1573843774803161090 “Zoom In: An Introduction to Circuits,” 2020: https://distill.pub/2020/circuits/zoom-in/ Geirhos et al., “ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness,” ICLR 2019: https://openreview.net/forum?id=Bygh9j09KX Dehghani et al., “Scaling Vision Transformers to 22 Billion Parameters,” 2023: https://arxiv.org/abs/2302.05442 Hasson et al., “Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks,” February 2020: https://www.gwern.net/docs/ai/scaling/2020-hasson.pdf

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

4.6 / 5

7 ratings

Spotify

5.0 / 5

12 ratings

Podcast Authority Score: 46 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

59

Social presence

0

YouTube

0

Engagement

85

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